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All Journal JOURNAL SCIENTIFIC OF MANDALIKA (JSM) Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI)
Irfan Irfan
Universitas Muhammadiyah Sinjai
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Other

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Implementasi Algoritma K-Means untuk Mengelompokkan Data Tingkat Kemiskinan di Sulawesi Selatan Berdasarkan Kota/Kabupaten Irfan; Faizal, Lut
Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Vol 7 No 2 (2024): Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Lamappapoleonro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57093/jisti.v7i2.220

Abstract

Poverty is a state in which individuals or groups lack sufficient access to the economic, social, and cultural resources required to attain a reasonable standard of living. The Central Bureau of Statistics (BPS) data offers an overview of the poverty rate in South Sulawesi province, although it only presents the poverty rate categorized by district or city. In order to prioritize their answers, the government must possess knowledge about the regions with the highest and lowest poverty rates to effectively address poverty. Hence, it is crucial to categorize districts or municipalities in South Sulawesi according to their poverty rate. This would enable the government to develop suitable policies or strategies to alleviate poverty while considering the poverty rate in each district or municipality. The data clustering in this study was performed using the K-Means algorithm and the RapidMiner program. The findings revealed the presence of four distinct clusters of districts or municipalities, which were categorized according to their poverty levels. The clusters are categorized as follows: Cluster 0 consists of Jeneponto, Gowa, Pangkep, Luwu, and North Luwu. Cluster 1 includes Bulukumba, Takalar, Maros, Wajo, Pinrang, Enrekang, Tana Toraja, and North Toraja. Cluster 2 comprises Bone and Makassar. Lastly, Cluster 3 consists of Selayar Islands, Bantaeng, Sinjai, Barru, Soppeng, Sidrap, East Luwu, Pare-Pare, and Palopo. Cluster 2 has the greatest poverty rate or number among all the clusters.
Optimalisasi Penentuan Bonus Karyawan dengan Sistem Pendukung Keputusan Metode TOPSIS di PT. Elizabeth Hanjaya Siti Nur Asia; M Noor Fuad; Husna Saleh; Muhammad As'ad; Irfan Irfan
Journal Scientific of Mandalika (JSM) e-ISSN 2745-5955 | p-ISSN 2809-0543 Vol. 6 No. 4 (2025)
Publisher : Institut Penelitian dan Pengembangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/10.36312/vol6iss4pp996-1009

Abstract

PT. Elizabeth Hanjaya is a retail company that provides various fashion products, PT. Elizabeth Hanjaya is a retail company that offers a variety of fashion products such as bags, shoes, accessories, and fashion supplies for men and women. This study uses the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method to build a decision support system in determining employee bonus recipients in this company. The purpose of the study is to design a system that can assess the feasibility of receiving employee bonuses based on the TOPSIS method. This method works by choosing an alternative that has the closest distance from the positive ideal solution and the furthest from the negative ideal solution based on geometric principles. The system is designed using Unified Modeling Language (UML) and developed with the PHP programming language. The results of the study show that the assessment system is able to produce accurate decisions. Based on testing with four alternatives, the TOPSIS method determines alternative 1 as the employee who is most eligible to receive a bonus with a final value of 0.675360. In addition, user evaluation results showed a high level of satisfaction, with 92% of respondents giving a "strongly agree" rating.
Co-Authors Husna Saleh Lut Faizal M Noor Fuad Muhammad As'ad Siti Nur Asia